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Developing a semantic framework for ...

Aydar, Mehmet.

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Developing a semantic framework for healthcare information interoperability.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Developing a semantic framework for healthcare information interoperability./
作者:	Aydar, Mehmet.
面頁冊數:	107 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-05(E), Section: B.
Contained By:	Dissertation Abstracts International77-05B(E).
標題:	Health sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3739325
ISBN:	9781339299143

Developing a semantic framework for healthcare information interoperability.
Aydar, Mehmet.

Developing a semantic framework for healthcare information interoperability. - 107 p.

Source: Dissertation Abstracts International, Volume: 77-05(E), Section: B.

Thesis (Ph.D.)--Kent State University, 2015.

Interoperability in healthcare is stated as the ability of health information systems to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities. The current healthcare information technology environment breeds incredibly complex data ecosystems. In many cases pertinent patient records are collected in multiple systems, often supplied by competing manufacturers with diverse data formats. This causes inefficiencies in data interoperability, as different formats of data create barriers in exchanging health information. This dissertation presents a semantic framework for healthcare information interoperability. We propose a system for translation of healthcare instance data, based on structured mapping definitions and using RDF as a common information representation to achieve semantic interoperability between different data models. Moreover, we introduce an entity similarity metric that utilizes the Jaccard index with the common relations of the data entities and common string literal words referenced by the data entities and augmented with data entity neighbors similarity. The precision of the similarity metric is enhanced by incorporating the auto-generated importance weights of the entity descriptors in the RDF representation of the dataset. Furthermore, we provide an automatic classification method, which we call summary graph generation, based on the pairwise entity similarities, and we propose that the summary graph can further be utilized for interoperability purposes. Finally, we present a suggestion based semi-automatic instance matching system and we test it on the RDF representation of a healthcare dataset. The system utilizes the entity similarity metric, and it presents similar node pairs to the user for possible instance matching. Based on the user feedback, it merges the matched nodes and suggests more matching pairs depending on the common relations and neighbors of the already matched nodes. We propose that the instance matching technique could be leveraged for mapping between separate data models.

ISBN: 9781339299143Subjects--Topical Terms:

3168359
Health sciences.

Developing a semantic framework for healthcare information interoperability.
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